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《Journal of Guangxi Normal University(Natural Science Edition)》 2018-04
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Infrared-Visible Target Tracking Based on AdaBoost Confidence Map

ZHANG Canlong;SU Jiancai;LI Zhixin;WANG Zhiwen;Guangxi Key Lab of Multi-source Information Mining &Security,Guangxi Normal University;Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing;College of Computer Science and Communication Engineering,Guangxi University of Science and Technology;  
To address the problem that the tracker is easy to drift away from the target and even failure in complex scenes,this paper presents an infrared-visible target tracking algorithm based on AdaBoost confidence map.Firstly,the target samples and background samples in infrared-visible images are characterized by using color and texture descriptor and are classified using AdaBoost classifier,and then the confidence maps of infrared and visible images are calculated based on the classification scores.Secondly,the similarity between confidence maps of target candidate and its template is calculated for visible and infrared images,and the visible similarity and infrared similarity are integrated into a jointobjective function by weighting.Finally,ajoint target location-shift formula is induced by performing multi-variable Taylor series expansion and maximization on the objective function,and the optimal target location is gained recursively by applying the mean shift procedure.The experimental result in infrared-visible image sequences demonstrates that the proposed method performs well in dealing with illumination change,target intersection,target occlusion and so on.
【Fund】: 国家自然科学基金(61866004 61663004 61462008 61751213);; 广西自然科学基金(2017GXNSFAA198365 2016GXNSFAA380146);; 柳州市科学研究与技术开发工程项目(2016C050205)
【CateGory Index】: TP391.41
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